def decision_tree(X_train,
X_test,
y_train,
y_test,
*,
max_depth=None,
random_state=None):
dTree = DecisionTreeRegressor(max_depth=max_depth,
random_state=random_state)
model = str(dTree) + '\n\nwithout Pruning'
fit_start = time.time()
dTree.fit(X_train, y_train)
fit_end = time.time()
fit_time = fit_end - fit_start
pred_start = time.time()
y_prediction = dTree.predict(X_test)
pred_end = time.time()
pred_time = pred_end - pred_start
evaluation.save_errors(y_test, y_prediction, model, fit_time, pred_time)
evaluation.print_errors(y_test, y_prediction, model, fit_time, pred_time)
def random_forest(X_train,
X_test,
y_train,
y_test,
*,
n_estimators=10,
criterion='mse',
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.0,
max_features='auto',
max_leaf_nodes=None,
min_impurity_decrease=0.0,
min_impurity_split=None,
bootstrap=True,
oob_score=False,
n_jobs=1,
random_state=None,
verbose=0,
warm_start=False):
regr = RandomForestRegressor(
n_estimators=n_estimators,
criterion=criterion,
max_depth=max_depth,
min_samples_split=min_samples_split,
min_samples_leaf=min_samples_leaf,
min_weight_fraction_leaf=min_weight_fraction_leaf,
max_features=max_features,
max_leaf_nodes=max_leaf_nodes,
min_impurity_decrease=min_impurity_decrease,
min_impurity_split=min_impurity_split,
bootstrap=bootstrap,
oob_score=oob_score,
n_jobs=n_jobs,
random_state=random_state,
verbose=verbose,
warm_start=warm_start)
model = str(regr)
fit_start = time.time()
regr.fit(X_train, y_train)
fit_end = time.time()
fit_time = fit_end - fit_start
pred_start = time.time()
y_prediction = regr.predict(X_test)
pred_end = time.time()
pred_time = pred_end - pred_start
evaluation.save_errors(y_test, y_prediction, model, fit_time, pred_time)
evaluation.print_errors(y_test, y_prediction, model, fit_time, pred_time)
def dtree_with_pruning(X_train, X_test, y_train, y_test, *, max_depth=None,
random_state=None):
# Erstellen und Trainieren des ursprünglichen Baumes
dtree = DecisionTreeRegressor(max_depth=max_depth,
random_state=random_state)
model = str(dtree) + '\n\nwith Pruning (Legacy)'
fit_start = time.time()
dtree.fit(X_train, y_train)
fit_end = time.time()
fit_time = fit_end - fit_start
pred_start = time.time()
# Erstellen einer Liste zum Speichern der ge-prunten Bäume
tree_array = [dtree]
num_nodes = dtree.tree_.capacity
# Pruning der Bäume und Anhängen an die Liste
k = 1
while num_nodes > 1:
tree_array.append(copy.deepcopy(tree_array[k - 1]))
min_node_idx, min_gk = models.dtree.prune.determine_alpha(
tree_array[k].tree_)
models.dtree.prune.prune(tree_array[k].tree_, min_node_idx)
num_nodes = sum(1 * (tree_array[k].tree_.n_node_samples != 0))
k += 1
# Finden des besten Baumes, basierend auf den Test-Daten
predictlist = []
for i in range(0, len(tree_array)):
pred = tree_array[i].predict(X_test)
# predictlist.append(tree_array[i].score(X_test, y_test))
predictlist.append(mean_squared_error(y_test, pred))
tree_scores = np.array(predictlist)
index = tree_scores.argmin()
pred = tree_array[index].predict(X_test)
pred_end = time.time()
pred_time = pred_end - pred_start
evaluation.save_errors(y_test, pred, model,
fit_time, pred_time)
evaluation.print_errors(y_test, pred, model,
fit_time, pred_time)
def svm_regression(X_train,
X_test,
y_train,
y_test,
*,
kernel='rbf',
degree=3,
gamma='auto',
coef0=0.0,
tol=0.001,
C=1.0,
epsilon=0.1,
shrinking=True,
cache_size=200,
verbose=False,
max_iter=-1):
svmr = SVR(kernel=kernel,
degree=degree,
gamma=gamma,
coef0=coef0,
tol=tol,
C=C,
epsilon=epsilon,
shrinking=shrinking,
cache_size=cache_size,
verbose=verbose,
max_iter=max_iter)
svmr_model = svmr
svmr_fit_start = time.time()
svmr.fit(X_train, y_train)
svmr_fit_end = time.time()
svmr_fit_time = svmr_fit_end - svmr_fit_start
svmr_pred_start = time.time()
pred = svmr.predict(X_test)
svmr_pred_end = time.time()
svmr_pred_time = svmr_pred_end - svmr_pred_start
evaluation.save_errors(y_test, pred, svmr_model, svmr_fit_time,
svmr_pred_time)
evaluation.print_errors(y_test, pred, svmr_model, svmr_fit_time,
svmr_pred_time)
def dtree_with_pruning_faster(X_train, X_test, y_train, y_test, *,
max_depth=None,
random_state=None):
# Initiate model
dtree = DecisionTreeRegressor(max_depth=max_depth,
random_state=random_state)
model = str(dtree) + '\n\nwith Pruning (Faster) '
# Fit model
fit_start = time.time()
dtree.fit(X_train, y_train)
fit_end = time.time()
fit_time = fit_end - fit_start
pred_start = time.time()
# Pruning trees
tree_pruner = models.dtree.prune_faster.TreePruner(dtree)
tree_pruner.run()
# Calculating errors
test_errors = []
train_errors = []
for tree in tree_pruner.trees:
y_pred_test = tree.predict(X_test)
test_errors.append(mean_squared_error(y_test, y_pred_test))
y_pred_train = tree.predict(X_train)
train_errors.append(mean_squared_error(y_train, y_pred_train))
# Find the best tree based on test data
test_errors_np = np.array(test_errors)
index = test_errors_np.argmin()
pred = tree_pruner.trees[index].predict(X_test)
pred_end = time.time()
pred_time = pred_end - pred_start
evaluation.save_errors(y_test, pred, model,
fit_time, pred_time)
evaluation.print_errors(y_test, pred, model,
fit_time, pred_time)
plt.tight_layout()
plt.show()
train_X, test_X, train_y, test_y = features.heart.split_train_test(
cleaned_data, "AHD")
train_X, test_X, train_y, test_y = features.heart.scale_to_train(
[train_X, test_X, train_y, test_y], [0, 2, 3, 6, 8, 9, 10, 18],
"minmax")
print(train_X)
# log reg with simple feature set
print("Evaluating simple feature set")
#log_reg = lm.SGDClassifier(n_jobs=10, loss="log", max_iter = 50)
log_reg = lm.LogisticRegression()
log_reg.fit(train_X, train_y)
pred = log_reg.predict(test_X)
pred_proba = log_reg.predict_proba(test_X)
evaluation.print_errors(test_y, pred)
print("")
"""
# log reg with advanced feature set
print("Evaluating modified feature set")
log_reg2 = lm.SGDClassifier(n_jobs=1, loss="log", max_iter=50)
classifier.fit(log_reg2, input_data2, targets)
pred, pred_proba = classifier.predict(log_reg2, input_data2)
evaluation.print_errors(targets, pred)
"""
if __name__ == '__main__':
data_train = pd.read_csv("data/zip.train", header = None, sep =" ")
cleaned_train_data = data_train.dropna(axis=1, thresh=2)
input_data = cleaned_train_data.iloc[:, 1:].values
targets = cleaned_train_data[0].values
input_data2 = features.zip_codes.multires(input_data)
# log reg with simple feature set
print("Evaluating simple feature set")
log_reg = lm.SGDClassifier(n_jobs=1, loss="log", max_iter = 50)
classifier.fit(log_reg, input_data, targets)
pred, pred_proba = classifier.predict(log_reg, input_data)
evaluation.print_errors(targets, pred)
print("")
# log reg with advanced feature set
print("Evaluating modified feature set")
log_reg2 = lm.SGDClassifier(n_jobs=1, loss="log", max_iter=50)
classifier.fit(log_reg2, input_data2, targets)
pred, pred_proba = classifier.predict(log_reg2, input_data2)
evaluation.print_errors(targets, pred)
clean_data = features.encode_binary(clean_data)
clean_data = features.encode_category(clean_data, 'ChestPain')
clean_data = features.encode_category(clean_data, 'Thal')
data_train, data_test = features.split(clean_data, 0.2)
X_train, y_train, X_test, y_test = features.set_target(
data_train, data_test, 'AHD')
logReg = lm.LogisticRegression()
print(clean_data.head())
classifier.fit(logReg, X_train, y_train)
pred, pred_proba = classifier.predict(logReg, X_test)
evaluation.print_errors(y_test, pred)
#print(len(data_train), len(data_test))
#print(clean_data)
# cleaned_train_data = data_train.dropna(axis=1, thresh=2)
#
# input_data = cleaned_train_data.iloc[:, 1:].values
# targets = cleaned_train_data[0].values
#
# input_data2 = features.zip_codes.multires(input_data)
#
# # log reg with simple feature set
# print("Evaluating simple feature set")
# log_reg = lm.SGDClassifier(n_jobs=1, loss="log", max_iter = 50)
#
